Automatic Vaginal Bacteria Segmentation and Classification Based on Superpixel and Deep Learning

Song, Youyi; Dong, Ni; Zeng, Zhongming; He, Liang; Chen, Siping; Lei, Baiying; Wang, Tianfu

doi:10.1166/jmihi.2014.1320

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…However, the discriminative power of these methods is limited due to the computational costs of identifying definitive features for subset characterization and optimization. Deep learning is a relatively new method in the field of artificial intelligence and machine learning technologies 25 26 27 28 29 30 31 . This approach has achieved considerable successes in multiple applications, including medical research.…”

mentioning

confidence: 99%

“…This approach has achieved considerable successes in multiple applications, including medical research. Deep convolutional neural networks were employed to medical image classification 31 ; deep belief nets and active learning were presented for multi-level gene and miRNA feature selection 25 ; convolutional neural networks were used to demonstrate an explicit gradient for feature complexity in the ventral pathway of the human brain 26 ; deep learning was applied to determine the sequence specificities of DNA and RNA-binding proteins for identifying causal disease variants 27 ; superpixel and deep learning were used for automatic vaginal bacteria segmentation and classification 28 ; some deep learning-based latent feature representations are proposed for diagnosis of Alzheimer’s disease and its prodromal stage, mild cognitive impairment (MCI), such as stacked auto-encoder and deep boltzmann machine 32 33 . However, only few works have explored deep learning methods to address the automatic classification of identified lesions on mammography.…”

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confidence: 99%

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Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Wang

Yang

Cai

et al. 2016

Sci Rep

224

131

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Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.

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confidence: 99%

mentioning

confidence: 99%

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Wang

Yang

Cai

et al. 2016

Sci Rep

224

131

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“…In a late situation, the significance of profound learning has spread past both the scholarly world and industry with a few motivating a genuine application since it is another, opportune and promising zone of machine learning. Likewise, the importance of profound learning are being broadened into alternate fields like Social media [P18][P19], Social system examination [20], bioinformatics [21], drug and medicinal services [22].…”

Section: Literature Surveymentioning

confidence: 99%

An Ontology Based System for Healthcare People to Prevent Cardiovascular Diseases

2019

IJRTE

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Nowadays the health care providers play a very important role in human health care. Currently, the health issues are addressed in traditional hospital systems by conducting several investigations to predict the type of diseases like diabetes mellitus, cardiovascular diseases, nephrological diseases, etc. This investigation doesn’t provide any early signs of human health care system. As being a human, have a chance of occurring cardiovascular disease, it is one of the most common diseases worldwide so, it may be considered as a main cause of death. The proposed model will predict the human health status based on activities perform by him to prevent cardiovascular diseases. So, in case of health care centre to represent current health care status using Social Networks, having different conventional methods, in that ontology is one among them. WordSet is the source for ontology where the information is present. These information’s are presented in the deep semantic web, these are considered as input to determine the cardiovascular health status based on the activity of a person shared in online Social Networks allowing access between persons and places.

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“…In recent years, deep learning algorithms can also be considered as a useful tool for detection and classification of bacteria colonies automatically [24][25][26]. Deep learning is an artificial intelligence algorithm that uses several layers and as layers progress, it extracts higher-level features from a given input.…”

Section: Introductionmentioning

confidence: 99%

Detection of Escherichia Coli Bacteria in Water Using Deep Learning

Yanık

Kaloğlu

Değirmenci

2020

Teh. glas. (Online)

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Considering its importance for vital activities, water and particularly drinking water should be clean and should not contain disease-causing bacteria. One of the pathogenic bacteria found in water is the bacterium Escherichia coli (E. coli). In the commonly used method for the detection of E. coli bacteria, the bacteria samples distilled from the water sample are seeded in endo agar medium and the change in the color of the medium as a result of the metabolic activities of the bacteria is examined with the naked eye. This color change can be seen with the human eye in approximately 22 ± 2 hours. In this study, a new bacteria detection scheme is proposed – using deep learning to detect E. coli bacteria both in shorter time and in practical way. The proposed technique is tested with experimentally collected data. Results show that the detection of bacteria can be done automatically within 6-10 hours with the proposed method.

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Automatic Vaginal Bacteria Segmentation and Classification Based on Superpixel and Deep Learning

Cited by 8 publications

References 9 publications

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

An Ontology Based System for Healthcare People to Prevent Cardiovascular Diseases

Detection of Escherichia Coli Bacteria in Water Using Deep Learning

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